The increasing dimensionality of the PES with the increase of number of atoms requires a large number of training datasets to fit the PES. Thus, the use of lower-level methods such as density functional theory (DFT) and second-order Møller-Plesset perturbation (MP2) theory is understandable, but probably not accurate enough for precise investigations like the one here targeted. To circumvent this bottleneck, researchers are applying ML approaches to bring a PES based on a low-level of electronic structure theory (DFT or MP2) to a higher level (CCSD(T)) one. One way to achieve this is by means of the \(\Delta \) -machine learning (\(\Delta \) -ML) approach, in which a correction is made to a property dataset obtained using an efficient, low-level ab initio theory such as DFT or MP2.[1]}, [2]}, [3]}, [4]}, [5]}, [6]}
